Vincent Bakker

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.

Domestic energy management methodology for optimizing efficiency in Smart Grids

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached.

A three-step methodology to improve domestic energy efficiency

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.

On simulating the effect on the energy efficiency of smart grid technologies

Most residential-used electricity is nowadays generated at inefficient central power plants consuming environmental unfriendly resources like coal or natural gas. However, a trend towards distributed generation, distributed storage and demand side load management is seen to improve the energy efficiency. In order to analyze the impact and requirements of these emerging technologies and control methodologies, good simulation models and software is required. In this paper, an improved simulator is presented to model (domestic) energy usage to analyze control strategies and improved technology on the system as a whole. Compared to the previous model, this model is more expressive and allows more future scenarios to be analyzed. Due to the added complexity, the model is extended such that the simulation can be distributed over multiple computers to reduce simulation time.

Planning the production of a fleet of domestic combined heat and power generators

This paper describes a planning problem, arising in the energy supply chain, that deals with the planning of the production runs of micro combined heat and power (microCHP) appliances installed in houses, cooperating in a fleet. Two types of this problem are described. The first one is the Single House Planning Problem (SHPP), where the focus is on supplying heat in the household. The second one combines many microCHPs into a Fleet Planning Problem (FPP) and focuses on the mutual electricity output, while still considering the local heat demand in the individual households. The problem is modeled as an ILP. For practical use a local search method is developed for the FPP, based on a dynamic programming formulation of the SHPP.

Integration of heat pumps in distribution grids: Economic motivation for grid control

Electric heat pumps combined with heat buffers are important elements in smart grids since they together allow to shift the consumption of electricity in time. In this paper the effects of different control algorithms for heat pumps on the investment costs for distribution grids are investigated. For this, an optimization approach is implemented for a case study within an area where the buildings are only supplied by electricity. The simulations use real smart meter data to generate realistic load curves of households and heat pumps. The calculations show that grid costs increase up to 71% with an inappropriate control and decrease by 10% with an optimal integration of heat pumps. Furthermore, the costs for the reinforcement of the grid are confronted with the benefits on consumer side using flexible price signals. The cost-benefit analysis shows that considering grid restrictions in the context of controllable devices is highly recommended.

The microCHP scheduling problem

The increasing penetration of renewable energy sources, the demand for more energy efficient electricity production and the increase in distributed electricity generation causes a shift in the way electricity is produced and consumed. The downside of these changes in the electricity grid is that network stability and controllability become more difficult compared to the old situation. The new network has to accommodate various means of production, consumption and buffering and needs to offer control over the energy flows between these three elements. In order to offer such a control mechanism we need to know more about the individual aspects. In this paper we focus on the modelling of distributed production. Especially, we look at the use of microCHP (Combined Heat and Power) appliances in a group of houses. The problem of planning the production runs of the microCHP is modelled via an ILP formulation, both for a single house and for a group of houses.

Comparing demand side management approaches

Due to increasing energy prices and the greenhouse effect, a more efficient energy supply is desirable, preferably based on renewable sources. To cope with the decrease of flexibility due to the introduction of renewables in production side of the supply chain, a more flexible consumer side is required. In literature, a lot of management methodologies are proposed to exploit the flexibility of consuming devices. Due to the large number of devices, these methodologies are often based on cost functions. Comparing the results of different methodologies is hard since differences in results can be caused by the methodologies itself or by less good defined cost functions. In this paper an auction based realtime control is compared with an ILP based realtime control using identical cost functions. Furthermore, the influence of planning on these strategies is studied. The simulation results show that planning leads to better and more predictable results. Furthermore, planning is best combined with an auction. However, the auction should slightly be altered to decrease communication and to be able to cope with (local) grid constraints.

Controlling the heating mode of heat pumps with the TRIANA three step methodology

Heat pump based heating systems are increasingly becoming an economic and efficient alternative for domestic gas heating systems. Concentrations of heat pump installations do consume large amounts of electricity, causing significant grid distribution and stability issues when the diversity factor is low. In this work, the three step control methodology TRIANA is extended to support the control of a heat pump fleet in order to improve diversity. Simulations show that TRIANA can reduce the peak load by at least 25% and improve σ by 33% for a representative soil-water scenario. Mathematical optimization shows that further improvement is possible.

Improved Heat Demand Prediction of Individual Households

One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators.